Methods and apparatus for image processing

ABSTRACT

A system includes a non-transitory computer readable medium storing instructions and a processor coupled to the non-transitory computer readable medium. The processor is configured to execute the instructions to obtain an input indicative of a desired image texture quality, receive an image captured by an image capturing device, analyze texture of the image, and generate a signal to vary or maintain a parameter of the image capturing device based on the analysis of the texture to yield the desired image texture quality.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 15/293,761,filed Oct. 14, 2016, which is a continuation of InternationalApplication No. PCT/CN2015/080995, filed Jun. 8, 2015, the disclosuresof both of which are hereby incorporated by reference in their entirety.

BACKGROUND

Images may be captured and processed for use in a variety of fields, forexample, computer vision. In computer vision, information contained inan image can be deciphered with the aid of processors. Computer visionmay aim to duplicate the abilities of human vision (e.g., recognition ofobjects, space, etc) by electronically perceiving physical contents ofimages.

Existing approaches for image acquisition and/or image processing may beless than optimal in some instances. For example, algorithms for imagecapture and/or processing may optimize an image for human vision (e.g.,human observation), and not for computer vision. Sub-optimally capturedor processed images may have detrimental effect on applications whichutilize computer vision, for example, navigation of autonomous vehicles,event detection, process control, etc.

SUMMARY

Embodiments disclosed herein provide systems and methods to imagecapture and/or processing. In many embodiments, imaging devices may beused to collect information regarding the surrounding environment. Theimage data obtained from the imaging devices can be processed for use incomputer vision. The suitability of use for computer vision may dependon an image texture quality. Advantageously, the approaches describedherein may provide improved extraction of spatial and temporalinformation from image data and may be used to improve the functionalityof applications which utilize the image data.

Thus, in one aspect, a method of processing an image captured by animage capture device is provided. The method comprising: receiving theimage captured by the image capture device; performing, with aid of aprocessor, an analysis of texture of the image; and varying ormaintaining a parameter of the image capture device based on theanalysis of the texture to yield a desired texture quality of asubsequent image.

In some embodiments, the analysis of texture of the image includesanalyzing feature points of the image. In some embodiments, theparameter is exposure time and/or gain of the image capture device. Insome embodiments, the parameter of the image capture device is varied ormaintained within 1 second of receiving the image. In some embodiments,varying or maintaining the parameter of the image capture device resultsin varying or maintaining a brightness of the subsequent image. In someembodiments, the method further comprises using the subsequent image ina computer vision process. In some embodiments, the method furthercomprises receiving the subsequent image captured by the image capturedevice using the varied or maintained parameter. In some embodiments,the varied parameter is obtained a) by applying a positive or negativepredetermined offset to the parameter of the image capture device orb)by multiplying or dividing the parameter of the image capture device bya predetermined amount. In some embodiments, the method furthercomprises using the processor to determine how the parameter of theimage capture device is to be varied or maintained. In some embodiments,the processor makes the determination using an AEC/AGC algorithm. Insome embodiments, the texture of the image is analyzed using a FAST orHarris corner detection algorithm. In some embodiments, the analysis ofthe texture of the image includes determining a number or distributionof feature points in the image. In some embodiments, the analysis of thetexture of the image includes determining a quality of feature points inthe image. In some embodiments, the image capture device is on board anunmanned aerial vehicle (UAV). In some embodiments, the image capturedevice includes a lens configured to direct light onto an image sensor.In some embodiments, the desired texture quality includes meeting and/orexceeding a predetermined threshold condition. In some embodiments, thepredetermined threshold condition is determined based on a planned useof the image or the subsequent image in a computer vision application.In some embodiments, the predetermined threshold condition includes athreshold distribution or quality of feature points.

In another aspect, an image processing apparatus is provided. Theapparatus comprises: an image capture device configured to capture animage; and one or more processors, individually or collectivelyconfigured to: receive the image captured by the image capture device;perform an analysis of texture of the image; and generate a signal tovary or maintain a parameter of the image capture device based on theanalysis of the texture to yield a desired texture quality of asubsequent image.

In some embodiments, the analysis of texture of the image includes ananalysis of feature points of the image. In some embodiments, theparameter is exposure time and/or gain of the image capture device. Insome embodiments, the parameter of the image capture device is varied ormaintained within 1 second of receiving the image. In some embodiments,the signal varies or maintains a brightness of the subsequent image. Insome embodiments, the subsequent image is used in a computer visionprocess. In some embodiments, the subsequent image is further receivedby the one or more processors. In some embodiments, the varied parameteris obtained a) by applying a positive or negative predetermined offsetto the parameter of the image capture device orb) by multiplying ordividing the parameter of the image capture device by a predeterminedamount. In some embodiments, the one or more processors determine howthe parameter of the image capture device is to be varied or maintained.In some embodiments, the one or more processors makes the determinationusing an AEC/AGC algorithm. In some embodiments, the texture of theimage is analyzed using a FAST or Harris corner detection algorithm. Insome embodiments, the analysis of the texture of the image includesdetermining a number or distribution of feature points in the image. Insome embodiments, the analysis of the texture of the image includesdetermining a quality of feature points in the image. In someembodiments, the image capture device is on board an unmanned aerialvehicle (UAV). In some embodiments, the image capture device includes alens configured to direct light onto an image sensor. In someembodiments, the desired texture quality includes meeting and/orexceeding a predetermined threshold condition. In some embodiments, thepredetermined threshold condition is determined based on a planned useof the image or the subsequent image in a computer vision application.In some embodiments, the predetermined threshold condition includes athreshold distribution or quality of feature points.

In another aspect, an image processor is provided. The processor isconfigured to: receive an image captured by an image capture device;perform an analysis of texture of the image; and generate a signal tovary or maintain a parameter of the image capture device based on theanalysis of the texture to yield a desired texture quality of asubsequent image.

In some embodiments, the analysis of texture of the image includes ananalysis of feature points of the image. In some embodiments, theparameter is exposure time and/or gain of the image capture device. Insome embodiments, the parameter of the image capture device is varied ormaintained within 1 second of receiving the image. In some embodiments,the signal varies or maintains a brightness of the subsequent image. Insome embodiments, the subsequent image is used in a computer visionprocess. In some embodiments, the subsequent image is further receivedby the image processor. In some embodiments, the varied parameter isobtained a) by applying a positive or negative predetermined offset tothe parameter of the image capture device orb) by multiplying ordividing the parameter of the image capture device by a predeterminedamount. In some embodiments, the processor is further configured todetermine how the parameter of the image capture device is to be variedor maintained. In some embodiments, the processor makes thedetermination using an AEC/AGC algorithm. In some embodiments, thetexture of the image is analyzed using a FAST or Harris corner detectalgorithm. In some embodiments, the analysis of the texture of the imageincludes determining a number or distribution of feature points in theimage. In some embodiments, the analysis of the texture of the imageincludes determining a quality of feature points in the image. In someembodiments, the image capture device is on board an unmanned aerialvehicle (UAV). In some embodiments, the image capture device includes alens configured to direct light onto an image sensor. In someembodiments, the desired texture quality includes meeting and/orexceeding a predetermined threshold condition. In some embodiments, thepredetermined threshold condition is determined based on a planned useof the image or the subsequent image in a computer vision application.In some embodiments, the predetermined threshold condition includes athreshold distribution or quality of feature points.

In another aspect, a method of processing an image is provided. Themethod comprises: receiving a plurality of images, wherein each of theplurality of images are captured using an image capture device underconditions that vary at least one parameter of the image capture device;performing, with aid of a processor, an analysis of texture of each ofthe plurality of images; and selecting one or more images from saidplurality based on the analyzed texture of each of the plurality ofimages.

In some embodiments, performing an analysis of texture of each of theplurality of images includes comprises performing an analysis of featurepoints of each of the plurality of images. In some embodiments, theplurality of images include at least three images. In some embodiments,the plurality of images are captured by the image capture device within0.5 seconds. In some embodiments, the parameter is exposure time and/orgain of the image capture device, and the plurality of images arecaptured using different exposure times and/or different gains. In someembodiments, the at least one parameter of the image capture device isvaried a) by applying a positive or negative predetermined offset to theat least one parameter of the image capture device orb) by multiplyingor dividing the at least one parameter of the image capture device by apredetermined amount. In some embodiments, the selected image(s) yieldsa desired texture quality relative to other images of said plurality ofimages. In some embodiments, the method further comprises using theimage capture device to capture a subsequent image using the sameparameter of the image capture device that was used in capturing theselected image(s). In some embodiments, the method further comprisesusing the subsequent image in a computer vision process. In someembodiments, the selected image(s) meets or exceeds a predeterminedthreshold condition. In some embodiments, the predetermined thresholdcondition is determined based on a planned use of one or more of theplurality of images in a computer vision application. In someembodiments, the predetermined threshold condition includes a thresholddistribution or quality of feature points. In some embodiments, themethod further comprises using the processor to determine how theparameter of the image capture device is to be varied. In someembodiments, the processor makes the determination using an AEC/AGCalgorithm. In some embodiments, the texture of the image is analyzedusing a FAST or Harris corner detection algorithm. In some embodiments,the analysis of the texture of the image includes determining a numberor distribution of feature points in the image. In some embodiments, theanalysis of the texture of the image includes determining a quality offeature points in the image. In some embodiments, the image capturedevice is on board an unmanned aerial vehicle (UAV). In someembodiments, the image capture device includes a lens configured todirect light onto an image sensor.

In another aspect, an image processing apparatus is provided. Theapparatus comprises: an image capture device configured to capture animage; and one or more processors, individually or collectivelyconfigured to: receive a plurality of images, wherein each of theplurality of images are captured using the image capture device underconditions that vary at least one parameter of the image capture device;perform an analysis of texture of each of the plurality of images; andselect one or more images from said plurality based on the analyzedtexture of each of the plurality of images.

In some embodiments, the analysis of texture of each of the plurality ofimages comprises an analysis of feature points of each of the pluralityof images. In some embodiments, the plurality of images include at leastthree images. In some embodiments, the plurality of images are capturedby the image capture device within 0.5 seconds. In some embodiments, theparameter is exposure time and/or gain of the image capture device, andthe plurality of images are captured using different exposure timesand/or different gains. In some embodiments, the at least one parameterof the image capture device is varied a) by applying a positive ornegative predetermined offset to the at least one parameter of the imagecapture device or b) by multiplying or dividing the at least oneparameter of the image capture device by a predetermined amount. In someembodiments, the selected image(s) yields a desired texture qualityrelative to other images of said plurality of images. In someembodiments, the image capture device is configured to capture asubsequent image using the same parameter of the image capture devicethat was used in capturing the selected image(s). In some embodiments,the subsequent image is used in a computer vision process. In someembodiments, the selected image(s) meets or exceeds a predeterminedthreshold condition. In some embodiments, the predetermined thresholdcondition is determined based on a planned use of one or more of theplurality of images in a computer vision application. In someembodiments, the predetermined threshold condition includes a thresholddistribution or quality of feature points. In some embodiments, the oneor more processors are configured to determine how the parameter of theimage capture device is to be varied. In some embodiments, the one ormore processors makes the determination using an AEC/AGC algorithm. Insome embodiments, the texture of the image is analyzed using a FAST orHarris corner detection algorithm. In some embodiments, the analysis ofthe texture of the image includes determining a number or distributionof feature points in the image. In some embodiments, the analysis of thetexture of the image includes determining a quality of feature points inthe image. In some embodiments, the image capture device is on board anunmanned aerial vehicle (UAV). In some embodiments, the image capturedevice includes a lens configured to direct light onto an image sensor.

In another aspect, an image processor is provided. The processor isconfigured to: receive a plurality of images, wherein each of theplurality of images are captured using an image capture device underconditions that vary at least one parameter of the image capture device;perform an analysis of texture of each of the plurality of images; andselect one or more images from said plurality based on the analyzedtexture of each of the plurality of images.

In some embodiments, the processor is configured to perform an analysisof feature points of each of the plurality of images. In someembodiments, the plurality of images include at least three images. Insome embodiments, the plurality of images are captured by the imagecapture device within 0.5 seconds. In some embodiments, the parameter isexposure time and/or gain of the image capture device, and the pluralityof images are captured using different exposure times and/or differentgains. In some embodiments, the at least one parameter of the imagecapture device is varied a) by applying a positive or negativepredetermined offset to the at least one parameter of the image capturedevice orb) by multiplying or dividing the at least one parameter of theimage capture device by a predetermined amount. In some embodiments, theselected image(s) yields a desired texture quality relative to otherimages of said plurality of images. In some embodiments, the imagecapture device is configured to capture a subsequent image using thesame parameter of the image capture device that was used in capturingthe selected image(s). In some embodiments, the subsequent image is usedin a computer vision process. In some embodiments, the selected image(s)meets or exceeds a predetermined threshold condition. In someembodiments, the predetermined threshold condition is determined basedon a planned use of one or more of the plurality of images in a computervision application. In some embodiments, the predetermined thresholdcondition includes a threshold distribution or quality of featurepoints. In some embodiments, the processor is configured determine howthe parameter of the image capture device is to be varied. In someembodiments, the processor makes the determination using an AEC/AGCalgorithm. In some embodiments, the texture of the image is analyzedusing a FAST or Harris corner detection algorithm. In some embodiments,the analysis of the texture of the image includes determining a numberor distribution of feature points in the image. In some embodiments, theanalysis of the texture of the image includes determining a quality offeature points in the image. In some embodiments, the image capturedevice is on board an unmanned aerial vehicle (UAV). In someembodiments, the image capture device includes a lens configured todirect light onto an image sensor.

In another aspect, a method of processing an image is provided. Themethod comprises: receiving an input indicative of a desired imagetexture quality; receiving the image captured by an image capturedevice; analyzing, with aid of a processor, texture of the image; andvarying or maintaining a parameter of the image capture device based onthe analysis of the texture to yield the desired image texture quality.

In some embodiments, analyzing texture of the image includes analyzingfeature points of the image. In some embodiments, varying a parameter ofthe image capture device include a) applying a positive or negativepredetermined offset to the parameter of the image capture device or b)multiplying or dividing the parameter of the image capture device by apredetermined amount. In some embodiments, the input indicative of thedesired texture quality includes a texture quality range or value. Insome embodiments, the input indicative of the desired texture qualityincludes an application for the image. In some embodiments, theapplication includes obstacle avoidance of a movable object thatcaptures the image with aid of an image sensor on board the movableobject. In some embodiments, analyzing the texture of the image includesanalyzing a distribution of feature points of the image. In someembodiments, the parameter of the image capturing device is varied ormaintained to yield a wider distribution of feature points when theapplication includes obstacle avoidance relative to other applications.In some embodiments, the application includes navigation of a movableobject that captures the image with aid of an image sensor on board themovable object. In some embodiments, analyzing the texture of the imageincludes analyzing a quality of feature points of the image. In someembodiments, the parameter of the image capturing device is varied ormaintained to yield a higher quality of feature points when theapplication includes navigation relative to other applications. In someembodiments, the input indicative of the desired texture quality isprovided manually by a user. In some embodiments, the input indicativeof the desired texture quality is generated with aid of the processor.In some embodiments, the processor is on-board a movable object.

In another aspect, an image processing apparatus is provided. Theapparatus comprises: an image capture device configured to capture animage; and one or more processors, individually or collectivelyconfigured to: receive an input indicative of a desired image texturequality; receive the image captured by the image capture device; analyzetexture of the image; and generate a signal to vary or maintain aparameter of the image capture device based on the analysis of thetexture to yield the desired image texture quality.

In some embodiments, an analysis of texture of the image includes ananalysis of feature points of the image. In some embodiments, the signala) applies a positive or negative predetermined offset to the parameterof the image capture device orb) multiplies or divides the parameter ofthe image capture device by a predetermined amount. In some embodiments,the input indicative of the desired texture quality includes a texturequality range or value. In some embodiments, the input indicative of thedesired texture quality includes an application for the image. In someembodiments, the application includes obstacle avoidance of a movableobject that captures the image with aid of an image sensor on board themovable object. In some embodiments, an analysis of the texture of theimage includes an analysis of a distribution of feature points of theimage. In some embodiments, the parameter of the image capturing deviceis varied or maintained to yield a wider distribution of feature pointswhen the application includes obstacle avoidance relative to otherapplications. In some embodiments, the application includes navigationof a movable object that captures the image with aid of an image sensoron board the movable object. In some embodiments, an analysis of thetexture of the image includes an analysis of a quality of feature pointsof the image. In some embodiments, the parameter of the image capturingdevice is varied or maintained to yield a higher quality of featurepoints when the application includes navigation relative to otherapplications. In some embodiments, the input indicative of the desiredtexture quality is provided manually by a user. In some embodiments, theinput indicative of the desired texture quality is generated with aid ofa processor. In some embodiments, the processor is on-board a movableobject.

In another aspect, an image processor is provided. The processor isconfigured to: receive an input indicative of a desired image texturequality; receive an image captured by an image capture device; analyzetexture of the image; and generate a signal to vary or maintain aparameter of the image capture device based on the analysis of thetexture to yield the desired image texture quality.

In some embodiments, an analysis of the texture of the image includesanalyzing feature points of the image. In some embodiments, the signala) applies a positive or negative predetermined offset to the parameterof the image capture device orb) multiplies or divides the parameter ofthe image capture device by a predetermined amount. In some embodiments,the input indicative of the desired texture quality includes a texturequality range or value. In some embodiments, the input indicative of thedesired texture quality includes an application for the image. In someembodiments, the application includes obstacle avoidance of a movableobject that captures the image with aid of an image sensor on board themovable object. In some embodiments, an analysis of the texture of theimage includes analyzing a distribution of feature points of the image.In some embodiments, the parameter of the image capturing device isvaried or maintained to yield a wider distribution of feature pointswhen the application includes obstacle avoidance relative to otherapplications. In some embodiments, the application includes navigationof a movable object that captures the image with aid of an image sensoron board the movable object. In some embodiments, an analysis of thetexture of the image includes analyzing a quality of feature points ofthe image. In some embodiments, the parameter of the image capturingdevice is varied or maintained to yield a higher quality of featurepoints when the application includes navigation relative to otherapplications. In some embodiments, the input indicative of the desiredtexture quality is provided manually by a user. In some embodiments, theinput indicative of the desired texture quality is generated with aid ofa processor. In some embodiments, the processor is on-board a movableobject.

It shall be understood that different aspects of the disclosure can beappreciated individually, collectively, or in combination with eachother. Various aspects of the disclosure described herein may be appliedto any of the particular applications set forth below. Other objects andfeatures of the present disclosure will become apparent by a review ofthe specification, claims, and appended figures.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present disclosure will be obtained by reference tothe following detailed description that sets forth illustrativeembodiments, in which the principles of the disclosure are utilized, andthe accompanying drawings of which:

FIG. 1 illustrates feature points detected in two images taken by animaging device operating under different parameters, in accordance withembodiments.

FIG. 2 illustrates feature points detected in two images taken by animaging device operating under different parameters, in accordance withembodiments.

FIG. 3 illustrates a method for processing an image captured by an imagecapture device, in accordance with embodiments.

FIG. 4 illustrates a method for processing feature points within animage captured by an image capture device, in accordance withembodiments.

FIG. 5 illustrates a method of processing a plurality of images, inaccordance with embodiments.

FIG. 6 illustrates a configuration of a texture detecting algorithmutilized in the context of computer vision, in accordance withembodiments.

FIG. 7 illustrates a UAV operating in an outdoor environment, inaccordance with embodiments.

FIG. 8 illustrates a UAV operating in an indoor environment, inaccordance with embodiments.

FIG. 9 illustrates an unmanned aerial vehicle (UAV), in accordance withembodiments.

FIG. 10 illustrates a movable object including a carrier and a payload,in accordance with embodiments.

FIG. 11 illustrates a schematic illustration by way of block diagram ofa system for controlling a movable object, in accordance withembodiments.

DETAILED DESCRIPTION

Image processing apparatuses and methods of processing an image areprovided. The apparatuses and methods may be used in various fields,such as computer vision and for various applications, such as autonomousnavigation of vehicles. In some embodiments, sensors may be used tocollect relevant data. Sensors as used herein may refer to any object ordevice used to gather information about the environment. Sensors may beof various types (e.g., biological, optical, chemical, mechanical, etc)and may detect and/or measure objects, events, stimuli, and conditions.Both living organisms and inanimate objects may have various sensors forgathering information about the environment. For example, in humans,optical sensors (e.g., the eye) may be used to acquire up to 80% ofexternal sensory information. In an imaging device (e.g., a camera),optical sensors (e.g., a CCD image sensor) may be used to captureimages. Optical sensors may also be referred to as image sensors.

Images captured by optical sensors of imaging devices may be utilized ina variety of contexts. For example, images captured by a camera may beused by human beings for any purpose (e.g., enjoyment, evidence in atrial, etc). Alternatively or in conjunction, images captured by theoptical sensors of imaging devices may be used in computer vision.Computer vision as used herein may refer to any context in which the useof models to extract information from image data is involved. Thus, ifimages captured by a camera are analyzed by a processor for some purpose(e.g., scene reconstruction, event detection, video tracking, objectrecognition, learning, indexing, navigation, motion estimation, andimage restoration), it may be being used in computer vision.

Various algorithms may be utilized to alter certain parameters of theimaging devices. Altering parameters may alter brightness and/or textureof an image captured by the imaging device. For example, automaticexposure control (AEC) or automatic gain control (AGC) on a camera maybe utilized to control for brightness of captured images. Brightness maybe controlled by varying the amount of light that is captured by animaging device or artificially increasing or decreasing the lightcaptured by the imaging device. Exposure may relate to the amount oflight reaching image sensors. Gain may relate to multiplying the amountof light that have reached the image sensors. Parameters of an imagingdevice, such as the shutter speed or aperture of the camera may affectthe amount of light that is captured by the imaging device (e.g., imagesensors). Conventional AEC and AGC algorithms may set the default of thecamera such that the overall brightness of a captured image is mediumgray (e.g., 18% gray, 18% reflectance in visible light). An image whoseoverall brightness is of medium gray may be neither glaring nor dark inthe context of human vision. An image whose overall brightness is ofmedium gray may appear natural in the context of human vision.Traditionally, one or more processors may individually or collectivelyexecute one or more algorithms (e.g., AEC/AGC) to optimize overallbrightness for human vision. Systems and methods described herein mayimprove or optimize captured images for computer vision. Algorithms mayalso be utilized to alter certain parameters of previously capturedimages. For example, algorithms may subject captured images topost-processing by controlling for brightness. For example, differentimages having different exposure for the same object may be fused tocreate a high-dynamic-range (HDR) image. While HDR techniques mayimprove the clarity of part of images for motion images, blurring mayresult. Additionally, for HDR techniques, specific sensors may beneeded. The aforementioned algorithms may optimize images for humanvision by controlling for brightness of the images.

In the context of computer vision, controlling (e.g., optimizing) forbrightness may not be of primary importance. An image whose overallbrightness is 18% gray may be less suited for use in computer visionthan the same image whose overall brightness is, for example, 8%, 10%,12%, 14%, 16%, 22%, 24%, or 26% gray. A more relevant parameter tocontrol for with respect to computer vision may be a texture within animage. Texture as used herein may refer to a size and granularity,directionality and orientation, and/or regularity and randomness withinan image. Texture within an image may provide information about aspatial arrangement and/or color intensities in an image. Texture may beascertained by or have relation to feature points within an image.Texture may be comprised of texels in a regular or repeated pattern.Texture may depend on variations in pixel intensities within an image.Texture may depend on numbers feature points or quality of featurepoints in an image. For example, a number of edge pixels in a fixed-sizeregion, and/or the direction of the edges may be indicative of texturein that region. The brightness and texture within an image may or maynot be independent of one another. Brightness may be varied ormaintained to provide a desired texture of an image.

FIG. 1 illustrates feature points detected in two images taken by animaging device operating with different parameters, in accordance withembodiments. The left image 101 shows an image captured usingconventional AEC/AGC algorithms. The left figure may appear natural orhave distinguishable features in the context of human vision. Forexample, a human being may be able to infer that there is an electricaloutlet 103 on a wall perpendicular to a floor 105. The right image 107shows an image captured using an algorithm optimizing for certaincomputer vision applications. The right figure may appear overly bright(e.g., overexposed) and without determinable features to a human.However, in the context of computer vision, the right figure mayrepresent a superior image with more information that may be extractedand/or processed as shown by a greater number of feature points (e.g.,shown as circles). In this context, image 107 may have a greater texturequality than image 101. FIG. 2 illustrates feature points detected intwo images taken by an imaging device operating with differentparameters, in accordance with embodiments. 201 shows an image capturedusing conventional AEC/AGC algorithms while 203 shows an image capturedusing an algorithm optimizing for certain computer vision applications.Again, it can be seen that the image captured using an algorithmoptimizing for certain computer vision applications (e.g., obstacleavoidance) may contain much more feature points and may have a highertexture quality for that computer vision application than the imagecaptured using conventional AEC/AGC algorithms.

FIG. 3 illustrates a method for processing an image captured by an imagecapture device, in accordance with embodiments. An image capture devicemay herein be referred to as an imaging device. An imaging device can beconfigured to detect electromagnetic radiation (e.g., visible, infrared,and/or ultraviolet light) and generate image data based on the detectedelectromagnetic radiation. For example, an imaging device may include acharge-coupled device (CCD) sensor or a complementarymetal-oxide-semiconductor (CMOS) sensor that generates electricalsignals in response to wavelengths of light. The resultant electricalsignals can be processed to produce image data. The image data generatedby an imaging device can include one or more images, which may be staticimages (e.g., photographs), dynamic images (e.g., video), or suitablecombinations thereof. The image data can be polychromatic (e.g., RGB,CMYK, HSV) or monochromatic (e.g., grayscale, black-and-white, sepia).The imaging device may include a lens configured to direct light onto animage sensor.

In some embodiments, the imaging device can be a camera. A camera can bea movie or video camera that captures dynamic image data (e.g., video).A camera can be a still camera that captures static images (e.g.,photographs). A camera may capture both dynamic image data and staticimages. A camera may switch between capturing dynamic image data andstatic images. Although certain embodiments provided herein aredescribed in the context of cameras, it shall be understood that thepresent disclosure can be applied to any suitable imaging device, andany description herein relating to cameras can also be applied to anysuitable imaging device, and any description herein relating to camerascan also be applied to other types of imaging devices. A camera can beused to generate 2D images of a 3D scene (e.g., an environment, one ormore objects, etc.). The images generated by the camera can representthe projection of the 3D scene onto a 2D image plane. Accordingly, eachpoint in the 2D image corresponds to a 3D spatial coordinate in thescene. The camera may comprise optical elements (e.g., lens, mirrors,filters, etc). The camera may capture color images, greyscale image,infrared images, and the like.

The imaging device may capture an image or a sequence of images at aspecific image resolution. In some embodiments, the image resolution maybe defined by the number of pixels in an image. In some embodiments, theimage resolution may be greater than or equal to about 352×420 pixels,480×320 pixels, 720×480 pixels, 1280×720 pixels, 1440×1080 pixels,1920×1080 pixels, 2048×1080 pixels, 3840×2160 pixels, 4096×2160 pixels,7680×4320 pixels, or 15360×8640 pixels. The camera may be a 4 K cameraor a camera with a higher resolution.

The imaging device may capture a sequence of images at a specificcapture rate. In some embodiments, the sequence of images may becaptured standard video frame rates such as about 24p, 25p, 30p, 48p,50p, 60p, 72p, 90p, 100p, 120p, 300p, 50i, or 60i. In some embodiments,the sequence of images may be captured at a rate less than or equal toabout one image every 0.0001 seconds, 0.0002 seconds, 0.0005 seconds,0.001 seconds, 0.002 seconds, 0.005 seconds, 0.01 seconds, 0.02 seconds,0.05 seconds. 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second, 2seconds, 5 seconds, or 10 seconds. In some embodiments, the capture ratemay change depending on user input and/or external conditions (e.g.rain, snow, wind, unobvious surface texture of environment).

The imaging device may have adjustable parameters. Under differingparameters, different images may be captured by the imaging device whilesubject to identical external conditions (e.g., location, lighting). Theadjustable parameter may comprise exposure (e.g., exposure time, shutterspeed, aperture, film speed), gain, gamma, area of interest,binning/subsampling, pixel clock, offset, triggering, ISO, etc.Parameters related to exposure may control the amount of light thatreaches an image sensor in the imaging device. For example, shutterspeed may control the amount of time light reaches an image sensor andaperture may control the amount of light that reaches the image sensorin a given time. Parameters related to gain may control theamplification of a signal from the optical sensor. ISO may control thelevel of sensitivity of the camera to available light. Parameterscontrolling for exposure and gain may be collectively considered and bereferred to herein as EXPO.

In step 301, a processor may receive an input indicative of a desiredimage texture quality. The desired texture quality may be determined bythe processor based on the configuration of preference. The desiredtexture quality may be based on user input. The desired texture qualitymay vary depending on external conditions (brightness of environment).The desired texture quality may be preset prior to operation of theimaging device. The desired texture quality may be updated while theimaging device is turned off. The desired texture quality may be updatedwhile the imaging device is in operation. The desired texture qualitymay be stored in a memory operably coupled to a processor on or offimaging device. The desired texture quality may be downloaded from arouter, from a cloud server, from an external device, or other server.The external device may be a mobile device (e.g., tablet, smartphone,remote controller) or a stationary device (e.g., computer). The desiredtexture quality may vary depending on the application image data may beutilized in as mentioned herein (e.g., autonomous positioning vs.obstacle avoidance).

In step 302, a processor may receive the image captured by the imagecapture device. In some embodiments, a processor may comprise afield-programmable gate array (FPGA), application-specific integratedcircuit (ASIC), application-specific standard product (ASSP), digitalsignal processor (DSP), central processing unit (CPU), graphicsprocessing unit (GPU), vision processing unit (VPU), complexprogrammable logic devices (CPLD), and the like. In some embodiments,the processor may be an on-board a movable object (e.g., a UAV) or anembedded processor carried by the imaging device. Alternatively, theprocessor may be an off-board processor separated from the imagingdevice (e.g., at a ground station, communicating with a camera). Thealgorithm may detect parameters of an imaging device. The algorithm maydetermine a brightness of an image. The algorithm may execute aconventional AEC algorithm. The algorithm may execute a conventional AGCalgorithm. The AEC and/or AGC algorithms may control for a brightness ofan image and calculate a reference exposure time and/or a gain.

In step 304, a processor may perform an analysis of a texture of thereceived image. The analysis may involve determining a texture qualityof the image. Texture quality as used herein may refer to a suitabilityof the image for image processing (e.g., for use in computer vision).Images with poor texture quality not suitable for processing may provideinaccurate or inadequate data while images with high texture qualitysuitable for processing may provide accurate or adequate data. Texturequality may be related to, or be dependent on an image saliency. Imagesaliency can be used herein to refer to the extent to which images havefeatures that are easily distinguishable or “stand out,” e.g., from thebackground and/or surrounding image pixels. Any description hereinreferring to texture quality may also be applied to image saliency, andvice-versa. Texture quality may or may not be affected by the exposurelevel and/or contrast of the image. In some embodiments, texture qualitymay be determined using image gradient methods in which a gradient foreach pixel in the image can be calculated and the results can be used todetermine whether the image texture is sufficiently rich. An image witha richer texture may have larger gradients which may signify an imagewith a higher texture quality. In some embodiments, texture quality isassessed by feature detection as described below.

Image texture quality may be determined based on one or more featurepoints. Images captured by the imaging devices can be processed todetect one or more feature points present in the images. A feature pointcan be a portion of an image (e.g., an edge, corner, interest point,blob, ridge, etc.) that is uniquely distinguishable from the remainingportions of the image and/or other feature points in the image.Optionally, a feature point may be relatively invariant totransformations of the imaged object (e.g., translation, rotation,scaling) and/or changes in the characteristics of the image (e.g.,brightness, exposure). A feature point may be detected in portions of animage that is rich in terms of informational content (e.g., significant2D texture). A feature point may be detected in portions of an imagethat are stable under perturbations (e.g., when varying illumination andbrightness of an image).

Feature detection as described herein can be accomplished using variousalgorithms (e.g., texture detection algorithm) which may extract one ormore feature points from image data. The algorithms may additionallymake various calculations regarding the feature points. For example, thealgorithms may calculate a total number of feature points, or “featurepoint number.” The algorithms may also calculate a distribution offeature points. For example, the feature points may be widelydistributed within an image (e.g., image data) or a subsection of theimage. For example, the feature points may be narrowly distributedwithin an image (e.g., image data) or a subsection of the image. Thealgorithms may also calculate a quality of the feature points. In someinstances, the quality of feature points may be determined or evaluatedbased on a value calculated by algorithms mentioned herein (e.g., FAST,Corner detector, Harris, etc).

The algorithm may be an edge detection algorithm, a corner detectionalgorithm, a blob detection algorithm, or a ridge detection algorithm.In some embodiments, the corner detection algorithm may be a “Featuresfrom accelerated segment test” (FAST). In some embodiments, the featuredetector may extract feature points and make calculations regardingfeature points using FAST. In some embodiments, the feature detector canbe a Canny edge detector, Sobel operator, Harris &Stephens/Plessy/Shi-Tomasi corner detection algorithm, the SUSAN cornerdetector, Level curve curvature approach, Laplacian of Gaussian,Difference of Gaussians, Determinant of Hessian, MSER, PCBR, orGrey-level blobs, ORB, FREAK, or suitable combinations thereof.

Texture quality may be related to feature points detected in the image.For example, the texture quality may be related to a total number offeature points in an image, distribution of feature points within animage, or a quality of feature points in an image. The determinant oftexture quality may depend on a configuration of preference. Theconfiguration of preference may relate to a specific application ofcomputer vision an image is being used for. The configuration ofpreference may relate to user preference.

For example, if images received by a processor are being used inapplications related to obstacle avoidance for vehicles, a distributionof feature points and/or quality of feature points within the image maydetermine the texture quality of the image. For example, a total numberof fixed-size regions having a certain number of feature points maydetermine the texture quality of the image. A wide distribution offeature points in an image may signify an image having high texturequality and suited to be used in obstacle avoidance while a narrowdistribution of feature points may signify an image having low texturequality and not suited to be used in obstacle avoidance. In someinstances, if an image is divided into N portions (e.g., N areas ofequal size), the number of portions within N portions having at least Kfeature points may indicate a distribution, or texture quality of theimage. N and/or K may be any arbitrary number, such as about or morethan 1, 2, 3, 5, 10, 50, 100, 1000, 10000, and the like. For example, ifan image is divided into 25 portions, the number of portions within the25 portions having at least 1 feature point may be used to determine adistribution, or texture quality of the image. For example, if an imageis divided into 100 portions, the number of portions within the 100portions having at least 5 feature points may be used to determine adistribution, or texture quality of the image. A wide distribution offeature points within an image may signify that no obstacles are beingmissed. In such a case, a small number of total feature points may besufficient in determining obstacles, a vehicle's position, and inavoiding obstacles.

For example, if images received by a processor are being used inapplications related to navigation or self-positioning of a vehicle, aquality of the feature points may indicate the texture quality of theimage. In some instances, a higher quality of feature points may meanthat detected feature points have been detected in an image having agreater average intensity (e.g., greyscale intensity value) compared tothe an image of the same scene with a lesser average intensity. In someinstances, if the images are used for self-positioning or navigation, afewer number of feature points having a good quality may be desiredand/or sufficient. For self-positioning, the parameters of the cameramay be selected so that a fewer number of feature points are identifiedwithin an image. For example, an image may be overexposed such that theimage is over-saturated or underexposed such that the image isunder-saturated. The fewer feature points that are nevertheless detectedor identified may be higher quality than feature points that would havebeen identified using settings for obstacle avoidance.

The configuration of preference may be received by the processor. Theconfiguration of preference may be determined in real time by aprocessing unit coupled to the processor. The configuration ofpreference may be based on user input, may be preset prior to operationof the imaging device, be updated while the imaging device is turnedoff, be updated while the imaging device is in operation. Theconfiguration of preference may be stored in a memory operably coupledto a processor on or off the imaging device. The configuration ofpreference may be downloaded from a router, from a cloud server, from anexternal device, or other server. The external device may be a mobiledevice (e.g., tablet, smartphone, remote controller) or a stationarydevice (e.g., computer).

In step 306, the processor may vary or maintain a parameter of the imagecapture device based on the analysis of the texture to yield a desiredtexture quality of a subsequent image. The image device maintaining orvarying the parameter of the image capture device may result in varyingor maintaining a brightness of subsequent images. The desired texturequality may be determined by the processor based on the configuration ofpreference. The desired texture quality may be based on user input. Thedesired texture quality may be preset prior to operation of the imagingdevice. The desired texture quality may be updated while the imagingdevice is turned off. The desired texture quality may be updated whilethe imaging device is in operation. The desired texture quality dependon external factors (e.g., night time vs daytime, average brightness ofan area, etc). The desired texture quality may depend on an averagetexture quality of one or more previously received images. The desiredtexture quality may be stored in a memory operably coupled to aprocessor on or off imaging device. The desired texture quality may bedownloaded from a router, from a cloud server, from an external device,or other server. The external device may be a mobile device (e.g.,tablet, smartphone, remote controller) or a stationary device (e.g.,computer). The desired texture quality may vary depending on theapplication image data may be utilized in as mentioned herein (e.g.,autonomous positioning vs. obstacle avoidance). Whether the receivedimage has a desired texture quality may determine whether the processorshould vary or maintain a parameter of the image capture device.

The processor may generate a signal to maintain or vary (e.g., adjust)at least one parameter of the imaging device (e.g., EXPO) such that anew reference parameter is determined (e.g., new reference exposuretime, new gain, etc). The processor may maintain or vary the parameterof the image capture device within about 0.005, 0.01, 0.05, 0.1, 0.5, 1,2, 4, or 10 seconds of receiving the image. The new reference parametermay differ in at least one parameter compared to the old referenceparameter. The processor may automatically determine how to adjust theparameters of the imaging device such that a higher quality textureimage may be obtained. For example, if an image not meeting the desiredtexture quality was captured using a parameter EXPO, steps 301 through306 may be repeated but with a different parameter, for example EXPO*2,EXPO*3, EXPO*4, etc. If an image captured using the parameter EXPO*2yielded an image having a texture quality lower than that captured usingparameter EXPO, steps 301 through 306 may be repeated using a differentparameter, for example EXPO/2, EXPO/3, EXPO/4, etc. The process may berepeated until an image having a desired texture quality is acquired. Itis to be understood that the relationship between the differingparameters may be of any kind (e.g., multiplicative, divisional,additional, etc) and that there may be no mathematical relationshipamong the differing parameters. The at least one parameter of the imagecapture device may be varied by applying a positive or negativepredetermined offset to the adjustable parameter of the image capturedevice. For example, the shutter speed, aperture, and/or gain of theimage capture device may be decreased or increased to capture thesubsequent image.

Once an image having a desired texture quality is acquired, it may beoutput to a processor to be used in a computer vision process (e.g., forautonomous positioning of vehicles). Subsequent images may be acquiredusing the parameters utilized to acquire the image having a desiredtexture quality. The subsequent images may be output to a processor tobe used in a computer vision process (e.g., for autonomous positioningof vehicles). The output may be an image, texture information of theimage, or synthesized texture information of the image. In someembodiments, a processor utilized in a computer vision process may beseparate from the processor analyzing the image for texture informationand/or processor controlling the parameters of the image capture device(e.g., processor executing AEC/AGC algorithms). In some embodiments aprocessor utilized in a computer vision process may be the sameprocessor analyzing the image for texture information and/or processorcontrolling the parameters of the image capture device. The systems andmethods provided herein may include one or more processors thatindividually or collectively perform any of the steps mentioned herein.

Method 300 may be repeated at a desired rate. For example, method 300may be repeated for every image captured by the imaging device. In someembodiments, method 300 may be repeated for every few images captured bythe imaging device. For example, method 300 may be repeated for every 2,3, 4, 5, 10, 15, 20, 50, 100 images captured by the imaging device.Alternatively, the image capture rate may be independent of the rate atwhich method 300 is repeated. The rate may be less than or equal toabout 0.005 seconds, 0.002 seconds, 0.05 seconds, 0.01 seconds, 0.02seconds, 0.05 seconds. 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second,2 seconds, 5 seconds, 10 seconds, 20 seconds, 50 seconds, 100 seconds,200 seconds, 500 seconds, 1000 seconds, or 3600 seconds. Thus, forexample, while images may be captured by the imaging device every 0.05seconds as mentioned herein, method 300 may be repeated only every 10seconds. Alternatively, or in conjunction, steps method 300 may berepeated in response to an external event or condition. A detector maydetect a luminance of the external environment. If the luminance passesa certain threshold, the detector may send a signal to the one or moreprocessors which may trigger method 300 to be repeated.

FIG. 4 illustrates a method for processing feature points within animage captured by an image capture device, in accordance withembodiments. For method 400, a desired texture quality may be preset andmay involve no step of receiving an input indicative of a desired imagetexture quality. The desired texture quality may be as previouslydescribed herein. For example, the desired texture quality may bedetermined by the processor based on the configuration of preference.The desired texture quality may vary depending on external conditions(brightness of environment). The desired texture quality may be presetprior to operation of the imaging device. The desired texture qualitymay be updated while the imaging device is turned off. The desiredtexture quality may be updated while the imaging device is in operation.The desired texture quality may be stored in a memory operably coupledto a processor on or off imaging device. The desired texture quality maybe downloaded from a router, from a cloud server, from an externaldevice, or other server. In step 402, a processor may receive an imagecaptured by an image capture device, substantially as described in step302. In step 404, a processor may perform an analysis of the image,which includes analyzing feature points of the image, substantially asdescribed in step 304. In step 406, the processor may vary or maintain aparameter of the image capture device based on the analysis of thetexture to yield a desired texture quality of a subsequent image,substantially as described in step 306. Method 400 may be repeated at adesired rate, substantially as described for method 300.

FIG. 5 illustrates a method of processing a plurality of images, inaccordance with embodiments. In step 502, a processor may receive aplurality of images. The plurality of images may be about or more than2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, or 50 images. The pluralityof images may be substantially simultaneously captured or be capturedover time. The plurality of images may include images of the same orsubstantially similar scene. The plurality of images may include imagesof the same or substantially similar field of view (FOV). The field ofview of an imaging device may be the extent of the environment that isdetectable (e.g., visible) by the imaging device. The field of view maybe related to the angle of view, which may be measured by the angularextent of a given scene that is imaged by the imaging device. The angleof view of an imaging device may be at an angle of less than or about360°, 300°, 240°, 180°, 120°, 60°, 30°, 20°, 10°, 5°, or 1°. Theplurality of images may be captured using an image capture device at aspecific capture rate. In some embodiments, the images may be capturedat standard video frame rates such as about 24p, 25p, 30p, 48p, 50p,60p, 72p, 90p, 100p, 120p, 300p, 50i, or 60i. In some embodiments, theimages may be captured at a rate less than or equal to about once every0.0001 seconds, 0.0002 seconds, 0.0005 seconds, 0.001 seconds, 0.002seconds, 0.005 seconds, 0.002 seconds, 0.05 seconds, 0.01 seconds, 0.02seconds, 0.05 seconds. 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second,2 seconds, 5 seconds, or 10 seconds.

The plurality of images may be captured using an image capture deviceunder conditions that vary at least one parameter of the image capturedevice. In some embodiments, multiple parameters, such as 2, 3, 4, 5 ormore parameters may be varied. The parameters may be as described herein(e.g., exposure time or gain). For example, the parameter to be variedmay be an exposure time of the image capture device and the plurality ofimages may be captured using different exposure times. For example, theparameter to be varied may be a gain of the image capture device, andthe plurality of images may be captured using different gains. Forexample, two parameters to be varied may be an exposure (e.g., shutterspeed or aperture) and gain. It is to be understood that therelationship between the varied parameter may be of any kind (e.g.,multiplicative, divisional, additional, etc) and that there may be nomathematical relationship among the varied parameter. Thus, if theplurality of images contains three images, the three images may havebeen captured under a reference exposure and reference gain, 2*thereference exposure and ½*the reference gain, and 3*the referenceexposure and ¼*the reference gain. Alternatively, in some embodiments,if the plurality of images contains three images, the three images mayhave been captured under EXPO/2, EXPO, or EXPO*2, where EXPO is a variedparameter and as described herein. The at least one parameter of theimage capture device may be varied by applying a positive or negativepredetermined offset to the adjustable parameter of the image capturedevice. The predetermined offset may be a shutter speed, aperture,and/or gain of the image capture device.

In step 504, a processor may perform an analysis of a texture of each ofthe plurality of images received in step 502. The analysis may be aspreviously described (e.g., analyzing feature points of each of theplurality of images). In step 506, the processor may select an imagefrom the plurality of images based on the analyzed texture of each ofthe plurality of images. The processor may select an image having apreferable texture quality out of the plurality of images. For example,the preferable texture quality may be an image having the highesttexture quality, an image having the highest total number of featurepoints within an image, an image having a feature point number fallingin a desired range, an image having an uneven, predefined, and/orrecognizable distribution and/or pattern of feature points, etc. Theselected image may yield a desired texture quality relative to otherimages of the plurality of images. The processor may also additionallycompare the image having the preferable texture quality against adesired texture quality as described herein (e.g., absolute desiredtexture quality). The image having the preferable texture quality may beoutput to be used in a computer vision process. The output may be theimage itself, texture information of the image, or synthesized textureinformation of the image. The processor may apply the parameters used incapturing the image having the preferable texture quality to the imagecapture device in order to capture subsequent images. For example,subsequent images may be captured using the same exposure and/or gain.The determinant of texture quality and/or desired texture quality maydepend on a specific application of computer vision an image is beingused for, or a configuration of preference as described herein.

Subsequently captured images may be output to a processor to be used ina computer vision process. The output may be the subsequently capturedimage(s), texture information of the subsequently captured image(s), orsynthesized texture information of the subsequently captured image(s).Alternatively or in conjunction, the parameters used to capture theimage having the preferable texture quality may be applied to previouslycaptured images (e.g., the non-selected images within the plurality ofimages) and be output to a processor to be used in a computer visionprocess. In some embodiments, a processor utilized in a computer visionprocess may be separate from the processor analyzing the image fortexture information and/or processor controlling the parameters of theimage capture device (e.g., processor executing AEC/AGC algorithms). Insome embodiments a processor utilized in a computer vision process maybe the same processor analyzing the image for texture information and/orprocessor controlling the parameters of the image capture device.

Steps 502 through 506 may be repeated at a desired rate. The rate may beless than or equal to about 0.005 seconds, 0.002 seconds, 0.05 seconds,0.01 seconds, 0.02 seconds, 0.05 seconds. 0.1 seconds, 0.2 seconds, 0.5seconds, 1 second, 2 seconds, 5 seconds, 10 seconds, 20 seconds, 50seconds, 100 seconds, 200 seconds, 500 seconds, 1000 seconds, or 3600seconds. The rate at which method 500 is repeated may or may not berelated to the image capture rate of the imaging device.

In some embodiments, sensors and/or processors may be coupled withmovable objects. Movable objects may be an unmanned movable object, suchas an unmanned aerial vehicle. In some embodiments, the sensors maycomprise imaging devices such as cameras. One or more imaging devicesmay be carried by a UAV. Any description herein of UAVs may apply to anyother type of movable objects as desired. In some embodiments, theprocessor may be an embedded processor carried by the UAV.Alternatively, the processor may be separated from the UAV (e.g., at aground station, communicating with the UAV or a movable remotecontroller communicating with the UAV). The UAV may utilize the imagingdevices as described herein to carry out operations (e.g., in thecontext of computer vision). For example, the processors on the UAV mayanalyze the images captured by the imaging devices and use them inself-positioning applications and/or obstacle avoidance applications.The UAV may utilize computer vision to self-navigate within anenvironment. Self-navigation may include determining a local or globallocation of the UAV, orientation of the UAV, detection and avoidance ofobstacles, and the like. Imaging devices of the present disclosure canbe situated on any suitable portion of the UAV, such as above,underneath, on the side(s) of, or within a vehicle body of the UAV. Someimaging devices can be mechanically coupled to the UAV such that thespatial disposition and/or motion of the UAV correspond to the spatialdisposition and/or motion of the imaging device. The imaging devices canbe coupled to the UAV via a rigid coupling, such that the imaging devicedoes not move relative to the portion of the UAV to which it isattached. Alternatively, the coupling between the imaging device and theUAV can permit movement (e.g., translational or rotational movementrelative to the UAV) of the imaging device relative to the UAV. Thecoupling can be a permanent coupling or non-permanent (e.g., releasable)coupling. Suitable coupling methods can include adhesives, bonding,welding, and/or fasteners (e.g., screws, nails, pins, etc.). Optionally,the imaging device can be integrally formed with a portion of the UAV.Furthermore, the imaging device can be electrically coupled with aportion of the UAV (e.g., processing unit, control system, data storage)so as to enable the data collected by the imaging device to be used forvarious functions of the UAV (e.g., navigation, control, propulsion,communication with a user or other device, etc.), such as theembodiments discussed herein. The imaging device may be operably coupledwith a portion of the UAV (e.g., processing unit, control system, datastorage). One or more imaging devices may be situated on the UAV. Forexample, 1, 2, 3, 4, 5 or more imaging devices may be situated on theUAV. The one or more imaging devices may have the same FOV or adifferent FOV. Each of the one or more imaging devices may be coupled toone or more processors. Each of the one or more imaging devices mayindividually or collectively perform the methods mentioned herein. Theone or more imaging devices may capture images each with a differentdesired texture quality. The one or more imaging devices may captureimages each with a same desired texture quality. Each imaging device maycapture images what are utilized for the same or different function(e.g., computer vision application). For example, a UAV may be coupledwith two imaging devices, one which captures images that are utilizedfor obstacle avoidance, and another that captures images that areutilized for navigation or self-positioning.

FIG. 6 illustrates a configuration of a texture detecting algorithmutilized in the context of computer vision, in accordance withembodiments. Configuration 600 can be performed using any embodiment ofthe apparatus and methods described herein.

One or more processors 602 (e.g., FPGA) may be coupled to one or moreimage sensors 604 embedded in an imaging device. The processors may beembedded in an imaging device or may be separate from the imagingdevice. The processors may include algorithms for controlling parameters(e.g., exposure, gain) of the imaging device. The algorithms forcontrolling parameters of the imaging device may be conventional AEC/AGCalgorithms as described herein. The processors may output a referenceexposure and gain configuration to be adapted by the imaging device forthe acquisition of images. For example, the imaging device may vary theexposure time and amount by varying the shutter time or aperture size.

The image sensor 604, such as a CMOS or CCD sensor, may generateelectrical signals in response to wavelength of light hitting thesensors. The image sensors may be a part of the imaging devices asdescribed herein. The electrical signals generated may contain imageinformation. The image information may be sent to the one or moreprocessors 602. The image information may vary depending on theconfiguration of the parameters (e.g., exposure and gain) even underidentical external conditions (e.g., identical lighting, location, etc).The one or more processors may include a texture detecting algorithm.The texture detecting algorithm may analyze an image or a plurality ofimages for texture quality as described herein (e.g., by determining afeature point number). The one or more processors may compare thetexture information amongst the plurality of images and/or may comparethe texture information against a desired texture quality (e.g., athreshold total feature point number within the image). Whether tocompare the texture information amongst the plurality of images, whetherto compare the texture information against a desired texture quality,and the desired texture quality may depend on a configuration ofpreference output by a computer vision processing unit 606. In someinstances, the configuration of preference may depend on computer visionapplications to be utilized. The computer vision processing unit maydetermine an application in which an image or images may be used for(e.g., self-positioning or obstacle avoidance of vehicles) and relay theinformation to the one or more processors which may then compare thetexture information amongst the plurality of images and/or determine adesired texture quality and compare the texture information against adesired texture quality. The computer vision processing unit may or maynot comprise the one or more processors 602. The computer visionprocessing unit may be on board the imaging device. The computer visionprocessing unit may be on a movable vehicle such as a UAV. The computervision processing unit may be on a ground communicating with a cameraand/or a UAV.

Based on the comparison, the one or more processors may maintain oradjust algorithms controlling for the parameters of the imaging device(e.g., AEC/AGC). Subsequent images may be acquired using the maintainedor adjusted parameters. Subsequent images may be sent to the computervision processing unit to be processed and be used for computer visionapplications. Alternatively or in conjunction, the maintained oradjusted parameters may be applied to previously acquired images whichmay then be output to a processor to be used in computer visionapplications.

The image data output to the computer vision processing unit may beassessed and be used as a basis for accomplishing various operations.For instance, the information assessed from the image data can be usedas a basis for outputting signals to cause a UAV to navigate within theenvironment. The signal can include control signals for the propulsionsystem (e.g., rotors) of the UAV for effecting movement of the vehicle.The signal can cause the UAV to avoid obstacles and/or cause the UAV toposition itself autonomously within an environment. The apparatuses andmethods disclosed herein can improve the field of computer vision andmay improve UAV navigation, thereby enhancing the robustness andflexibility of UAV functionalities such as navigation and obstacleavoidance.

The embodiments provided herein can be applied to various types of UAVs.For instance, the UAV may be a small-scale UAV that weighs no more than10 kg and/or has a maximum dimension of no more than 1.5 m. In someembodiments, the UAV may be a rotorcraft, such as a multi-rotor aircraftthat is propelled to move through the air by a plurality of propellers(e.g., a quadcopter). Additional examples of UAVs and other movableobjects suitable for use with the embodiments presented herein aredescribed in further detail below.

The UAVs described herein can be operated completely autonomously (e.g.,by a suitable computing system such as an onboard controller),semi-autonomously, or manually (e.g., by a human user). The UAV canreceive commands from a suitable entity (e.g., human user or autonomouscontrol system) and respond to such commands by performing one or moreactions. For example, the UAV can be controlled to take off from theground, move within the air (e.g., with up to three degrees of freedomin translation and up to three degrees of freedom in rotation), move totarget location or to a sequence of target locations, hover within theair, land on the ground, and so on. As another example, the UAV can becontrolled to move at a specified velocity and/or acceleration (e.g.,with up to three degrees of freedom in translation and up to threedegrees of freedom in rotation) or along a specified movement path.Furthermore, the commands can be used to control one or more UAVcomponents, such as the components described herein (e.g., sensors,actuators, propulsion units, payload, etc.). For instance, some commandscan be used to control the position, orientation, and/or operation of aUAV payload such as a camera. Optionally, the UAV can be configured tooperate in accordance with one or more predetermined operating rules.The operating rules may be used to control any suitable aspect of theUAV, such as the position (e.g., latitude, longitude, altitude),orientation (e.g., roll, pitch yaw), velocity (e.g., translationaland/or angular), and/or acceleration (e.g., translational and/orangular) of the UAV. For instance, the operating rules can be designedsuch that the UAV is not permitted to fly beyond a threshold height,e.g., the UAV can be configured to fly at a height of no more than 400 mfrom the ground. In some embodiments, the operating rules can be adaptedto provide automated mechanisms for improving UAV safety and preventingsafety incidents. For example, the UAV can be configured to detect arestricted flight region (e.g., an airport) and not fly within apredetermined distance of the restricted flight region, thereby avertingpotential collisions with aircraft and other obstacles.

FIG. 7 illustrates a UAV 702 operating in an outdoor environment 700, inaccordance with embodiments. The outdoor environment 700 may be anurban, suburban, or rural setting, or any other environment that is notat least partially within a building. The UAV 702 may be operatedrelatively close to the ground 704 (e.g., low altitude) or relativelyfar from the ground 704 (e.g., high altitude). For example, a UAV 702operating less than or equal to approximately 10 m from the ground maybe considered to be at low altitude, while a UAV 702 operating atgreater than or equal to approximately 10 m from the ground may beconsidered to be at high altitude.

In some embodiments, the outdoor environment 700 includes one or moreobstacles 708 a-d. An obstacle may include any object or entity that mayobstruct the movement of the UAV 702. Some obstacles may be situated onthe ground 704 (e.g., obstacles 708 a, 708 d), such as buildings, groundvehicles (e.g., cars, motorcycles, trucks, bicycles), human beings,animals, plants (e.g., trees, bushes), and other manmade or naturalstructures. Some obstacles may be in contact with and/or supported bythe ground 704, water, manmade structures, or natural structures.Alternatively, some obstacles may be wholly located in the air 706(e.g., obstacles 708 b, 708 c), including aerial vehicles (e.g.,airplanes, helicopters, hot air balloons, other UAVs) or birds. Aerialobstacles may not be supported by the ground 704, or by water, or by anynatural or manmade structures. An obstacle located on the ground 704 mayinclude portions that extend substantially into the air 706 (e.g., tallstructures such as towers, skyscrapers, lamp posts, radio towers, powerlines, trees, etc.).

FIG. 8 illustrates a UAV 852 operating in an indoor environment 850, inaccordance with embodiments. The indoor environment 850 is within theinterior of a building 854 having a floor 856, one or more walls 858,and/or a ceiling or roof 860. Exemplary buildings include residential,commercial, or industrial buildings such as houses, apartments, offices,manufacturing facilities, storage facilities, and so on. The interior ofthe building 854 may be completely enclosed by the floor 856, walls 858,and ceiling 860 such that the UAV 852 is constrained to the interiorspace. Conversely, at least one of the floor 856, walls 858, or ceiling860 may be absent, thereby enabling the UAV 852 to fly from inside tooutside, or vice-versa. Alternatively or in combination, one or moreapertures 864 may be formed in the floor 856, walls 858, or ceiling 860(e.g., a door, window, skylight).

Similar to the outdoor environment 700, the indoor environment 850 caninclude one or more obstacles 862 a-d. Some obstacles may be situated onthe floor 856 (e.g., obstacle 862 a), such as furniture, appliances,human beings, animals, plants, and other manmade or natural objects.Conversely, some obstacles may be located in the air (e.g., obstacle 862b), such as birds or other UAVs. Some obstacles in the indoorenvironment 850 can be supported by other structures or objects.Obstacles may also be attached to the ceiling 860 (e.g., obstacle 862c), such as light fixtures, ceiling fans, beams, or otherceiling-mounted appliances or structures. In some embodiments, obstaclesmay be attached to the walls 858 (e.g., obstacle 862 d), such as lightfixtures, shelves, cabinets, and other wall-mounted appliances orstructures. Notably, the structural components of the building 854 canalso be considered to be obstacles, including the floor 856, walls 858,and ceiling 860.

The obstacles described herein may be substantially stationary (e.g.,buildings, plants, structures) or substantially mobile (e.g., humanbeings, animals, vehicles, or other objects capable of movement). Someobstacles may include a combination of stationary and mobile components(e.g., a windmill). Mobile obstacles or obstacle components may moveaccording to a predetermined or predictable path or pattern. Forexample, the movement of a car may be relatively predictable (e.g.,according to the shape of the road). Alternatively, some mobileobstacles or obstacle components may move along random or otherwiseunpredictable trajectories. For example, a living being such as ananimal may move in a relatively unpredictable manner.

In order to ensure safe and efficient operation, it may be beneficial toprovide the UAV with mechanisms for assessing environmental informationsuch as the locations of objects in the surrounding environment and itsstate information such as position, velocity, attitude, andacceleration. Additionally, accurate assessment of environmental andstate information can facilitate navigation, particularly when the UAVis operating in a semi-autonomous or fully autonomous manner and can bevaluable for a wide variety of UAV functionalities.

Accordingly, the UAVs described herein can include one or more sensorsconfigured to collect relevant data, such as information relating to theUAV state, the surrounding environment, or the objects within theenvironment. Based on the relevant data that is collected, it can bepossible to generate control signals for controlling UAV navigation.Exemplary sensors suitable for use with the embodiments disclosed hereininclude location sensors (e.g., global positioning system (GPS) sensors,mobile device transmitters enabling location triangulation), visionsensors (e.g., imaging devices capable of detecting visible, infrared,or ultraviolet light, such as cameras), proximity or range sensors(e.g., ultrasonic sensors, lidar, time-of-flight or depth cameras),inertial sensors (e.g., accelerometers, gyroscopes, inertial measurementunits (IMUs)), altitude sensors, attitude sensors (e.g., compasses)pressure sensors (e.g., barometers), audio sensors (e.g., microphones)or field sensors (e.g., magnetometers, electromagnetic sensors). Anysuitable number and combination of sensors can be used, such as one,two, three, four, five, six, seven, eight, or more sensors. Optionally,the data can be received from sensors of different types (e.g., two,three, four, five, six, seven, eight, or more types). Sensors ofdifferent types may measure different types of signals or information(e.g., position, orientation, velocity, acceleration, proximity,pressure, etc.) and/or utilize different types of measurement techniquesto obtain data. For instance, the sensors may include any suitablecombination of active sensors (e.g., sensors that generate and measureenergy from their own energy source) and passive sensors (e.g., sensorsthat detect available energy). As another example, some sensors maygenerate absolute measurement data that is provided in terms of a globalcoordinate system (e.g., position data provided by a GPS sensor,attitude data provided by a compass or magnetometer), while othersensors may generate relative measurement data that is provided in termsof a local coordinate system (e.g., relative angular velocity providedby a gyroscope; relative translational acceleration provided by anaccelerometer; relative attitude information provided by a visionsensor; relative distance information provided by an ultrasonic sensor,lidar, or time-of-flight camera). In some instances, the localcoordinate system may be a body coordinate system that is definedrelative to the UAV.

The sensors can be configured to collect various types of data, such asdata relating to the UAV, the surrounding environment, or objects withinthe environment. For example, at least some of the sensors may beconfigured to provide data regarding a state of the UAV. The stateinformation provided by a sensor can include information regarding aspatial disposition of the UAV (e.g., location or position informationsuch as longitude, latitude, and/or altitude; orientation or attitudeinformation such as roll, pitch, and/or yaw). The state information canalso include information regarding motion of the UAV (e.g.,translational velocity, translation acceleration, angular velocity,angular acceleration, etc.). A sensor can be configured, for instance,to determine a spatial disposition and/or motion of the UAV with respectto up to six degrees of freedom (e.g., three degrees of freedom inposition and/or translation, three degrees of freedom in orientationand/or rotation). The state information may be provided relative to aglobal coordinate system or relative to a local coordinate system (e.g.,relative to the UAV or another entity). For example, a sensor can beconfigured to determine the distance between the UAV and the usercontrolling the UAV, or the distance between the UAV and the startingpoint of flight for the UAV.

The data obtained by the sensors may provide various types ofenvironmental information. For example, the sensor data may beindicative of an environment type, such as an indoor environment,outdoor environment, low altitude environment, or high altitudeenvironment. The sensor data may also provide information regardingcurrent environmental conditions, including weather (e.g., clear, rainy,snowing), visibility conditions, wind speed, time of day, and so on.Furthermore, the environmental information collected by the sensors mayinclude information regarding the objects in the environment, such asthe obstacles described herein. Obstacle information may includeinformation regarding the number, density, geometry, and/or spatialdisposition of obstacles in the environment. For example, the obstaclesmentioned herein may be captured by an imaging device coupled to theUAV. A processor coupled to the imaging device may process the capturedimages. For example, the processor may extract feature points within theimage. The obstacles within a captured image may be described by featurepoints. The feature points may help in determining the existence ofobstacles and aid in computer vision applications, such as obstacleavoidance of UAVs.

The systems, devices, and methods described herein can be applied to awide variety of movable objects. As previously mentioned, anydescription herein of a UAV may apply to and be used for any movableobject. A movable object of the present disclosure can be configured tomove within any suitable environment, such as in air (e.g., a fixed-wingaircraft, a rotary-wing aircraft, or an aircraft having neither fixedwings nor rotary wings), in water (e.g., a ship or a submarine), onground (e.g., a motor vehicle, such as a car, truck, bus, van,motorcycle; a movable structure or frame such as a stick, fishing pole;or a train), under the ground (e.g., a subway), in space (e.g., aspaceplane, a satellite, or a probe), or any combination of theseenvironments. The movable object can be a vehicle, such as a vehicledescribed elsewhere herein. In some embodiments, the movable object canbe mounted on a living subject, such as a human or an animal. Suitableanimals can include avians, canines, felines, equines, bovines, ovines,porcines, delphines, rodents, or insects.

The movable object may be capable of moving freely within theenvironment with respect to six degrees of freedom (e.g., three degreesof freedom in translation and three degrees of freedom in rotation).Alternatively, the movement of the movable object can be constrainedwith respect to one or more degrees of freedom, such as by apredetermined path, track, or orientation. The movement can be actuatedby any suitable actuation mechanism, such as an engine or a motor. Theactuation mechanism of the movable object can be powered by any suitableenergy source, such as electrical energy, magnetic energy, solar energy,wind energy, gravitational energy, chemical energy, nuclear energy, orany suitable combination thereof. The movable object may beself-propelled via a propulsion system, as described elsewhere herein.The propulsion system may optionally run on an energy source, such aselectrical energy, magnetic energy, solar energy, wind energy,gravitational energy, chemical energy, nuclear energy, or any suitablecombination thereof. Alternatively, the movable object may be carried bya living being.

In some instances, the movable object can be a vehicle. Suitablevehicles may include water vehicles, aerial vehicles, space vehicles, orground vehicles. For example, aerial vehicles may be fixed-wing aircraft(e.g., airplane, gliders), rotary-wing aircraft (e.g., helicopters,rotorcraft), aircraft having both fixed wings and rotary wings, oraircraft having neither (e.g., blimps, hot air balloons). A vehicle canbe self-propelled, such as self-propelled through the air, on or inwater, in space, or on or under the ground. A self-propelled vehicle canutilize a propulsion system, such as a propulsion system including oneor more engines, motors, wheels, axles, magnets, rotors, propellers,blades, nozzles, or any suitable combination thereof. In some instances,the propulsion system can be used to enable the movable object to takeoff from a surface, land on a surface, maintain its current positionand/or orientation (e.g., hover), change orientation, and/or changeposition.

The movable object can be controlled remotely by a user or controlledlocally by an occupant within or on the movable object. In someembodiments, the movable object is an unmanned movable object, such as aUAV. An unmanned movable object, such as a UAV, may not have an occupantonboard the movable object. The movable object can be controlled by ahuman or an autonomous control system (e.g., a computer control system),or any suitable combination thereof. The movable object can be anautonomous or semi-autonomous robot, such as a robot configured with anartificial intelligence.

The movable object can have any suitable size and/or dimensions. In someembodiments, the movable object may be of a size and/or dimensions tohave a human occupant within or on the vehicle. Alternatively, themovable object may be of size and/or dimensions smaller than thatcapable of having a human occupant within or on the vehicle. The movableobject may be of a size and/or dimensions suitable for being lifted orcarried by a human. Alternatively, the movable object may be larger thana size and/or dimensions suitable for being lifted or carried by ahuman. In some instances, the movable object may have a maximumdimension (e.g., length, width, height, diameter, diagonal) of less thanor equal to about: 2 cm, 5 cm, 10 cm, 50 cm, 1 m, 2 m, 5 m, or 10 m. Themaximum dimension may be greater than or equal to about: 2 cm, 5 cm, 10cm, 50 cm, 1 m, 2 m, 5 m, or 10 m. For example, the distance betweenshafts of opposite rotors of the movable object may be less than orequal to about: 2 cm, 5 cm, 10 cm, 50 cm, 1 m, 2 m, 5 m, or 10 m.Alternatively, the distance between shafts of opposite rotors may begreater than or equal to about: 2 cm, 5 cm, 10 cm, 50 cm, 1 m, 2 m, 5 m,or 10 m.

In some embodiments, the movable object may have a volume of less than100 cm×100 cm×100 cm, less than 50 cm×50 cm×30 cm, or less than 5 cm×5cm×3 cm. The total volume of the movable object may be less than orequal to about: 1 cm³, 2 cm³, 5 cm³, 10 cm³, 20 cm³, 30 cm³, 40 cm³, 50cm³, 60 cm³, 70 cm³, 80 cm³, 90 cm³, 100 cm³, 150 cm³, 200 cm³, 300 cm³,500 cm³, 750 cm³, 1000 cm³, 5000 cm³, 10,000 cm³, 100,000 cm³, 1 m³, or10 m³. Conversely, the total volume of the movable object may be greaterthan or equal to about: 1 cm³, 2 cm³, 5 cm³, 10 cm³, 20 cm³, 30 cm³, 40cm³, 50 cm³, 60 cm³, 70 cm³, 80 cm³, 90 cm³, 100 cm³, 150 cm³, 200 cm³,300 cm³, 500 cm³, 750 cm³, 1000 cm³, 5000 cm³, 10,000 cm³, 100,000 cm³,1 m³, or 10 m³.

In some embodiments, the movable object may have a footprint (which mayrefer to the lateral cross-sectional area encompassed by the movableobject) less than or equal to about: 32,000 cm², 20,000 cm², 10,000 cm²,1,000 cm², 500 cm², 100 cm², 50 cm², 10 cm², or 5 cm². Conversely, thefootprint may be greater than or equal to about: 32,000 cm², 20,000 cm²,10,000 cm², 1,000 cm², 500 cm², 100 cm², 50 cm², 10 cm², or 5 cm².

In some instances, the movable object may weigh no more than 1000 kg.The weight of the movable object may be less than or equal to about:1000 kg, 750 kg, 500 kg, 200 kg, 150 kg, 100 kg, 80 kg, 70 kg, 60 kg, 50kg, 45 kg, 40 kg, 35 kg, 30 kg, 25 kg, 20 kg, 15 kg, 12 kg, 10 kg, 9 kg,8 kg, 7 kg, 6 kg, 5 kg, 4 kg, 3 kg, 2 kg, 1 kg, 0.5 kg, 0.1 kg, 0.05 kg,or 0.01 kg. Conversely, the weight may be greater than or equal toabout: 1000 kg, 750 kg, 500 kg, 200 kg, 150 kg, 100 kg, 80 kg, 70 kg, 60kg, 50 kg, 45 kg, 40 kg, 35 kg, 30 kg, 25 kg, 20 kg, 15 kg, 12 kg, 10kg, 9 kg, 8 kg, 7 kg, 6 kg, 5 kg, 4 kg, 3 kg, 2 kg, 1 kg, 0.5 kg, 0.1kg, 0.05 kg, or 0.01 kg.

In some embodiments, a movable object may be small relative to a loadcarried by the movable object. The load may include a payload and/or acarrier, as described in further detail below. In some examples, a ratioof a movable object weight to a load weight may be greater than, lessthan, or equal to about 1:1. In some instances, a ratio of a movableobject weight to a load weight may be greater than, less than, or equalto about 1:1. Optionally, a ratio of a carrier weight to a load weightmay be greater than, less than, or equal to about 1:1. When desired, theratio of an movable object weight to a load weight may be less than orequal to: 1:2, 1:3, 1:4, 1:5, 1:10, or even less. Conversely, the ratioof a movable object weight to a load weight can also be greater than orequal to: 2:1, 3:1, 4:1, 5:1, 10:1, or even greater.

In some embodiments, the movable object may have low energy consumption.For example, the movable object may use less than about: 5 W/h, 4 W/h, 3W/h, 2 W/h, 1 W/h, or less. In some instances, a carrier of the movableobject may have low energy consumption. For example, the carrier may useless than about: 5 W/h, 4 W/h, 3 W/h, 2 W/h, 1 W/h, or less. Optionally,a payload of the movable object may have low energy consumption, such asless than about: 5 W/h, 4 W/h, 3 W/h, 2 W/h, 1 W/h, or less.

FIG. 9 illustrates an unmanned aerial vehicle (UAV) 900, in accordancewith embodiments. The UAV may be an example of a movable object asdescribed herein. The UAV 900 can include a propulsion system havingfour rotors 902, 904, 906, and 908. Any number of rotors may be provided(e.g., one, two, three, four, five, six, seven, eight, or more). Therotors, rotor assemblies, or other propulsion systems of the unmannedaerial vehicle may enable the unmanned aerial vehicle to hover/maintainposition, change orientation, and/or change location. The distancebetween shafts of opposite rotors can be any suitable length 910. Forexample, the length 910 can be less than or equal to 2 m, or less thanequal to 5 m. In some embodiments, the length 910 can be within a rangefrom 40 cm to 1 m, from 10 cm to 2 m, or from 5 cm to 5 m. Anydescription herein of a UAV may apply to a movable object, such as amovable object of a different type, and vice versa.

In some embodiments, the movable object can be configured to carry aload. The load can include one or more of passengers, cargo, equipment,instruments, and the like. The load can be provided within a housing.The housing may be separate from a housing of the movable object, or bepart of a housing for an movable object. Alternatively, the load can beprovided with a housing while the movable object does not have ahousing. Alternatively, portions of the load or the entire load can beprovided without a housing. The load can be rigidly fixed relative tothe movable object. Optionally, the load can be movable relative to themovable object (e.g., translatable or rotatable relative to the movableobject).

In some embodiments, the load includes a payload. The payload can beconfigured not to perform any operation or function. Alternatively, thepayload can be a payload configured to perform an operation or function,also known as a functional payload. For example, the payload can includeone or more sensors for surveying one or more targets. Any suitablesensor can be incorporated into the payload, such as an image capturedevice (e.g., a camera), an audio capture device (e.g., a parabolicmicrophone), an infrared imaging device, or an ultraviolet imagingdevice. The sensor can provide static sensing data (e.g., a photograph)or dynamic sensing data (e.g., a video). In some embodiments, the sensorprovides sensing data for the target of the payload. Alternatively or incombination, the payload can include one or more emitters for providingsignals to one or more targets. Any suitable emitter can be used, suchas an illumination source or a sound source. In some embodiments, thepayload includes one or more transceivers, such as for communicationwith a module remote from the movable object. Optionally, the payloadcan be configured to interact with the environment or a target. Forexample, the payload can include a tool, instrument, or mechanismcapable of manipulating objects, such as a robotic arm.

Optionally, the load may include a carrier. The carrier can be providedfor the payload and the payload can be coupled to the movable object viathe carrier, either directly (e.g., directly contacting the movableobject) or indirectly (e.g., not contacting the movable object).Conversely, the payload can be mounted on the movable object withoutrequiring a carrier. The payload can be integrally formed with thecarrier. Alternatively, the payload can be releasably coupled to thecarrier. In some embodiments, the payload can include one or morepayload elements, and one or more of the payload elements can be movablerelative to the movable object and/or the carrier, as described above.

The carrier can be integrally formed with the movable object.Alternatively, the carrier can be releasably coupled to the movableobject. The carrier can be coupled to the movable object directly orindirectly. The carrier can provide support to the payload (e.g., carryat least part of the weight of the payload). The carrier can include asuitable mounting structure (e.g., a gimbal platform) capable ofstabilizing and/or directing the movement of the payload. In someembodiments, the carrier can be adapted to control the state of thepayload (e.g., position and/or orientation) relative to the movableobject. For example, the carrier can be configured to move relative tothe movable object (e.g., with respect to one, two, or three degrees oftranslation and/or one, two, or three degrees of rotation) such that thepayload maintains its position and/or orientation relative to a suitablereference frame regardless of the movement of the movable object. Thereference frame can be a fixed reference frame (e.g., the surroundingenvironment). Alternatively, the reference frame can be a movingreference frame (e.g., the movable object, a payload target).

In some embodiments, the carrier can be configured to permit movement ofthe payload relative to the carrier and/or movable object. The movementcan be a translation with respect to up to three degrees of freedom(e.g., along one, two, or three axes) or a rotation with respect to upto three degrees of freedom (e.g., about one, two, or three axes), orany suitable combination thereof.

In some instances, the carrier can include a carrier frame assembly anda carrier actuation assembly. The carrier frame assembly can providestructural support to the payload. The carrier frame assembly caninclude individual carrier frame components, some of which can bemovable relative to one another. The carrier actuation assembly caninclude one or more actuators (e.g., motors) that actuate movement ofthe individual carrier frame components. The actuators can permit themovement of multiple carrier frame components simultaneously, or may beconfigured to permit the movement of a single carrier frame component ata time. The movement of the carrier frame components can produce acorresponding movement of the payload. For example, the carrieractuation assembly can actuate a rotation of one or more carrier framecomponents about one or more axes of rotation (e.g., roll axis, pitchaxis, or yaw axis). The rotation of the one or more carrier framecomponents can cause a payload to rotate about one or more axes ofrotation relative to the movable object. Alternatively or incombination, the carrier actuation assembly can actuate a translation ofone or more carrier frame components along one or more axes oftranslation, and thereby produce a translation of the payload along oneor more corresponding axes relative to the movable object.

In some embodiments, the movement of the movable object, carrier, andpayload relative to a fixed reference frame (e.g., the surroundingenvironment) and/or to each other, can be controlled by a terminal. Theterminal can be a remote control device at a location distant from themovable object, carrier, and/or payload. The terminal can be disposed onor affixed to a support platform. Alternatively, the terminal can be ahandheld or wearable device. For example, the terminal can include asmartphone, tablet, laptop, computer, glasses, gloves, helmet,microphone, or suitable combinations thereof. The terminal can include auser interface, such as a keyboard, mouse, joystick, touchscreen, ordisplay. Any suitable user input can be used to interact with theterminal, such as manually entered commands, voice control, gesturecontrol, or position control (e.g., via a movement, location or tilt ofthe terminal).

The terminal can be used to control any suitable state of the movableobject, carrier, and/or payload. For example, the terminal can be usedto control the position and/or orientation of the movable object,carrier, and/or payload relative to a fixed reference from and/or toeach other. In some embodiments, the terminal can be used to controlindividual elements of the movable object, carrier, and/or payload, suchas the actuation assembly of the carrier, a sensor of the payload, or anemitter of the payload. The terminal can include a wirelesscommunication device adapted to communicate with one or more of themovable object, carrier, or payload.

The terminal can include a suitable display unit for viewing informationof the movable object, carrier, and/or payload. For example, theterminal can be configured to display information of the movable object,carrier, and/or payload with respect to position, translationalvelocity, translational acceleration, orientation, angular velocity,angular acceleration, or any suitable combinations thereof. In someembodiments, the terminal can display information provided by thepayload, such as data provided by a functional payload (e.g., imagesrecorded by a camera or other image capturing device).

Optionally, the same terminal may both control the movable object,carrier, and/or payload, or a state of the movable object, carrierand/or payload, as well as receive and/or display information from themovable object, carrier and/or payload. For example, a terminal maycontrol the positioning of the payload relative to an environment, whiledisplaying image data captured by the payload, or information about theposition of the payload. Alternatively, different terminals may be usedfor different functions. For example, a first terminal may controlmovement or a state of the movable object, carrier, and/or payload whilea second terminal may receive and/or display information from themovable object, carrier, and/or payload. For example, a first terminalmay be used to control the positioning of the payload relative to anenvironment while a second terminal displays image data captured by thepayload. Various communication modes may be utilized between a movableobject and an integrated terminal that both controls the movable objectand receives data, or between the movable object and multiple terminalsthat both control the movable object and receives data. For example, atleast two different communication modes may be formed between themovable object and the terminal that both controls the movable objectand receives data from the movable object.

FIG. 10 illustrates a movable object 1000 including a carrier 1002 and apayload 1004, in accordance with embodiments. Although the movableobject 1000 is depicted as an aircraft, this depiction is not intendedto be limiting, and any suitable type of movable object can be used, aspreviously described herein. One of skill in the art would appreciatethat any of the embodiments described herein in the context of aircraftsystems can be applied to any suitable movable object (e.g., an UAV). Insome instances, the payload 1004 may be provided on the movable object1000 without requiring the carrier 1002. The movable object 1000 mayinclude propulsion mechanisms 1006, a sensing system 1008, and acommunication system 1010.

The propulsion mechanisms 1006 can include one or more of rotors,propellers, blades, engines, motors, wheels, axles, magnets, or nozzles,as previously described. For example, the propulsion mechanisms 1006 maybe rotor assemblies or other rotary propulsion units, as disclosedelsewhere herein. The movable object may have one or more, two or more,three or more, or four or more propulsion mechanisms. The propulsionmechanisms may all be of the same type. Alternatively, one or morepropulsion mechanisms can be different types of propulsion mechanisms.The propulsion mechanisms 1006 can be mounted on the movable object 1000using any suitable means, such as a support element (e.g., a driveshaft) as described elsewhere herein. The propulsion mechanisms 1006 canbe mounted on any suitable portion of the movable object 1000, such onthe top, bottom, front, back, sides, or suitable combinations thereof.

In some embodiments, the propulsion mechanisms 1006 can enable themovable object 1000 to take off vertically from a surface or landvertically on a surface without requiring any horizontal movement of themovable object 1000 (e.g., without traveling down a runway). Optionally,the propulsion mechanisms 1006 can be operable to permit the movableobject 1000 to hover in the air at a specified position and/ororientation. One or more of the propulsion mechanisms 1000 may becontrolled independently of the other propulsion mechanisms.Alternatively, the propulsion mechanisms 1000 can be configured to becontrolled simultaneously. For example, the movable object 1000 can havemultiple horizontally oriented rotors that can provide lift and/orthrust to the movable object. The multiple horizontally oriented rotorscan be actuated to provide vertical takeoff, vertical landing, andhovering capabilities to the movable object 1000. In some embodiments,one or more of the horizontally oriented rotors may spin in a clockwisedirection, while one or more of the horizontally rotors may spin in acounterclockwise direction. For example, the number of clockwise rotorsmay be equal to the number of counterclockwise rotors. The rotation rateof each of the horizontally oriented rotors can be varied independentlyin order to control the lift and/or thrust produced by each rotor, andthereby adjust the spatial disposition, velocity, and/or acceleration ofthe movable object 1000 (e.g., with respect to up to three degrees oftranslation and up to three degrees of rotation).

The sensing system 1008 can include one or more sensors that may sensethe spatial disposition, velocity, and/or acceleration of the movableobject 1000 (e.g., with respect to up to three degrees of translationand up to three degrees of rotation). The one or more sensors caninclude global positioning system (GPS) sensors, motion sensors,inertial sensors, proximity sensors, or image sensors. The sensing dataprovided by the sensing system 1008 can be used to control the spatialdisposition, velocity, and/or orientation of the movable object 1000(e.g., using a suitable processing unit and/or control module, asdescribed below). Alternatively, the sensing system 1008 can be used toprovide data regarding the environment surrounding the movable object,such as weather conditions, proximity to potential obstacles, locationof geographical features, location of manmade structures, and the like.

The communication system 1010 enables communication with terminal 1012having a communication system 1014 via wireless signals 1016. Thecommunication systems 1010, 1014 may include any number of transmitters,receivers, and/or transceivers suitable for wireless communication. Thecommunication may be one-way communication, such that data can betransmitted in only one direction. For example, one-way communicationmay involve only the movable object 1000 transmitting data to theterminal 1012, or vice-versa. The data may be transmitted from one ormore transmitters of the communication system 1010 to one or morereceivers of the communication system 1012, or vice-versa.Alternatively, the communication may be two-way communication, such thatdata can be transmitted in both directions between the movable object1000 and the terminal 1012. The two-way communication can involvetransmitting data from one or more transmitters of the communicationsystem 1010 to one or more receivers of the communication system 1014,and vice-versa.

In some embodiments, the terminal 1012 can provide control data to oneor more of the movable object 1000, carrier 1002, and payload 1004 andreceive information from one or more of the movable object 1000, carrier1002, and payload 1004 (e.g., position and/or motion information of themovable object, carrier or payload; data sensed by the payload such asimage data captured by a payload camera). In some instances, controldata from the terminal may include instructions for relative positions,movements, actuations, or controls of the movable object, carrier and/orpayload. For example, the control data may result in a modification ofthe location and/or orientation of the movable object (e.g., via controlof the propulsion mechanisms 1006), or a movement of the payload withrespect to the movable object (e.g., via control of the carrier 1002).The control data from the terminal may result in control of the payload,such as control of the operation of a camera or other image capturingdevice (e.g., taking still or moving pictures, zooming in or out,turning on or off, switching imaging modes, change image resolution,changing focus, changing depth of field, changing exposure time,changing viewing angle or field of view). In some instances, thecommunications from the movable object, carrier and/or payload mayinclude information from one or more sensors (e.g., of the sensingsystem 1008 or of the payload 1004). The communications may includesensed information from one or more different types of sensors (e.g.,GPS sensors, motion sensors, inertial sensor, proximity sensors, orimage sensors). Such information may pertain to the position (e.g.,location, orientation), movement, or acceleration of the movable object,carrier and/or payload. Such information from a payload may include datacaptured by the payload or a sensed state of the payload. The controldata provided transmitted by the terminal 1012 can be configured tocontrol a state of one or more of the movable object 1000, carrier 1002,or payload 1004. Alternatively or in combination, the carrier 1002 andpayload 1004 can also each include a communication module configured tocommunicate with terminal 1012, such that the terminal can communicatewith and control each of the movable object 1000, carrier 1002, andpayload 1004 independently.

In some embodiments, the movable object 1000 can be configured tocommunicate with another remote device in addition to the terminal 1012,or instead of the terminal 1012. The terminal 1012 may also beconfigured to communicate with another remote device as well as themovable object 1000. For example, the movable object 1000 and/orterminal 1012 may communicate with another movable object, or a carrieror payload of another movable object. When desired, the remote devicemay be a second terminal or other computing device (e.g., computer,laptop, tablet, smartphone, or other mobile device). The remote devicecan be configured to transmit data to the movable object 1000, receivedata from the movable object 1000, transmit data to the terminal 1012,and/or receive data from the terminal 1012. Optionally, the remotedevice can be connected to the Internet or other telecommunicationsnetwork, such that data received from the movable object 1000 and/orterminal 1012 can be uploaded to a website or server.

FIG. 11 is a schematic illustration by way of block diagram of a system1100 for controlling a movable object, in accordance with embodiments.The system 1100 can be used in combination with any suitable embodimentof the systems, devices, and methods disclosed herein. The system 1100can include a sensing module 1102, processing unit 1104, non-transitorycomputer readable medium 1106, control module 1108, and communicationmodule 1110.

The sensing module 1102 can utilize different types of sensors thatcollect information relating to the movable objects in different ways.Different types of sensors may sense different types of signals orsignals from different sources. For example, the sensors can includeinertial sensors, GPS sensors, proximity sensors (e.g., lidar), orvision/image sensors (e.g., a camera). The sensing module 1102 can beoperatively coupled to a processing unit 1104 having a plurality ofprocessors. In some embodiments, the sensing module can be operativelycoupled to a transmission module 1112 (e.g., a Wi-Fi image transmissionmodule) configured to directly transmit sensing data to a suitableexternal device or system. For example, the transmission module 1112 canbe used to transmit images captured by a camera of the sensing module1102 to a remote terminal.

The processing unit 1104 can have one or more processors, such as aprogrammable processor (e.g., a central processing unit (CPU)). Theprocessing unit 1104 can be operatively coupled to a non-transitorycomputer readable medium 1106. The non-transitory computer readablemedium 1106 can store logic, code, and/or program instructionsexecutable by the processing unit 1104 for performing one or more steps.The non-transitory computer readable medium can include one or morememory units (e.g., removable media or external storage such as an SDcard or random access memory (RAM)). In some embodiments, data from thesensing module 1102 can be directly conveyed to and stored within thememory units of the non-transitory computer readable medium 1106. Thememory units of the non-transitory computer readable medium 1106 canstore logic, code and/or program instructions executable by theprocessing unit 1104 to perform any suitable embodiment of the methodsdescribed herein. For example, the processing unit 1104 can beconfigured to execute instructions causing one or more processors of theprocessing unit 1104 to analyze sensing data produced by the sensingmodule. The memory units can store sensing data from the sensing moduleto be processed by the processing unit 1104. In some embodiments, thememory units of the non-transitory computer readable medium 1106 can beused to store the processing results produced by the processing unit1104.

In some embodiments, the processing unit 1104 can be operatively coupledto a control module 1108 configured to control a state of the movableobject. For example, the control module 1108 can be configured tocontrol the propulsion mechanisms of the movable object to adjust thespatial disposition, velocity, and/or acceleration of the movable objectwith respect to six degrees of freedom. Alternatively or in combination,the control module 1108 can control one or more of a state of a carrier,payload, or sensing module.

The processing unit 1104 can be operatively coupled to a communicationmodule 1110 configured to transmit and/or receive data from one or moreexternal devices (e.g., a terminal, display device, or other remotecontroller). Any suitable means of communication can be used, such aswired communication or wireless communication. For example, thecommunication module 1110 can utilize one or more of local area networks(LAN), wide area networks (WAN), infrared, radio, WiFi, point-to-point(P2P) networks, telecommunication networks, cloud communication, and thelike. Optionally, relay stations, such as towers, satellites, or mobilestations, can be used. Wireless communications can be proximitydependent or proximity independent. In some embodiments, line-of-sightmay or may not be required for communications. The communication module1110 can transmit and/or receive one or more of sensing data from thesensing module 1102, processing results produced by the processing unit1104, predetermined control data, user commands from a terminal orremote controller, and the like.

The components of the system 1100 can be arranged in any suitableconfiguration. For example, one or more of the components of the system1100 can be located on the movable object, carrier, payload, terminal,sensing system, or an additional external device in communication withone or more of the above. Additionally, although FIG. 11 depicts asingle processing unit 1104 and a single non-transitory computerreadable medium 1106, one of skill in the art would appreciate that thisis not intended to be limiting, and that the system 1100 can include aplurality of processing units and/or non-transitory computer readablemedia. In some embodiments, one or more of the plurality of processingunits and/or non-transitory computer readable media can be situated atdifferent locations, such as on the movable object, carrier, payload,terminal, sensing module, additional external device in communicationwith one or more of the above, or suitable combinations thereof, suchthat any suitable aspect of the processing and/or memory functionsperformed by the system 1100 can occur at one or more of theaforementioned locations.

As used herein A and/or B encompasses one or more of A or B, andcombinations thereof such as A and B.

While some embodiments of the present disclosure have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the disclosure. It should beunderstood that various alternatives to the embodiments of thedisclosure described herein may be employed in practicing thedisclosure. It is intended that the following claims define the scope ofthe invention and that methods and structures within the scope of theseclaims and their equivalents be covered thereby.

What is claimed is:
 1. A system comprising: a non-transitory computerreadable medium storing instructions; and a processor coupled to thenon-transitory computer readable medium and configured to execute theinstructions to: obtain an input indicative of a desired image texturequality; receive an image captured by an image capturing device; analyzetexture of the image; and vary or maintain a parameter of the imagecapturing device based on the analysis of the texture to yield thedesired image texture quality.
 2. The system of claim 1, wherein theparameter of the image capturing device includes an exposure time and/ora gain of the image capturing device.
 3. The system of claim 1, whereinthe analysis of the texture of the image includes analyzing featurepoints of the image.
 4. The system of claim 1, wherein the signal:applies a positive or negative predetermined offset to the parameter ofthe image capture device, or multiplies or divides the parameter of theimage capture device by a predetermined amount.
 5. The system of claim1, wherein the input indicative of the desired texture quality includesa texture quality range or value.
 6. The system of claim 1, wherein theinput indicative of the desired texture quality includes an applicationfor the image.
 7. The system of claim 6, wherein: the applicationincludes obstacle avoidance for a movable object that carries the imagecapturing device; the analysis of the texture of the image includesanalyzing a distribution of feature points of the image; and theparameter of the image capturing device is varied or maintained to yielda wider distribution of feature points.
 8. The system of claim 6,wherein: the application includes navigation of a movable object thatcarries the image capturing device; the analysis of the texture of theimage includes analyzing a quality of feature points of the image; andthe parameter of the image capturing device is varied or maintained toyield a higher quality of feature points.
 9. The system of claim 1,wherein the input indicative of the desired texture quality is providedmanually by a user or generated by the processor.
 10. The system ofclaim 8, wherein the processor is on-board a movable object.
 11. Animage processing apparatus comprising: an image capturing device; andone or more processors, individually or collectively configured to:obtain an input indicative of a desired image texture quality; receivean image captured by the image capturing device; analyze texture of theimage; and vary or maintain a parameter of the image capturing devicebased on the analysis of the texture to yield the desired image texturequality.
 12. The apparatus of claim 11, wherein the parameter of theimage capturing device includes an exposure time and/or a gain of theimage capturing device.
 13. The apparatus of claim 11, wherein theanalysis of the texture of the image includes analyzing feature pointsof the image.
 14. The apparatus of claim 11, wherein the signal: appliesa positive or negative predetermined offset to the parameter of theimage capture device, or multiplies or divides the parameter of theimage capture device by a predetermined amount.
 15. The apparatus ofclaim 11, wherein the input indicative of the desired texture qualityincludes a texture quality range or value.
 16. The apparatus of claim11, wherein the input indicative of the desired texture quality includesan application for the image.
 17. The apparatus of claim 16, wherein:the application includes obstacle avoidance for a movable object thatcarries the image capturing device; the analysis of the texture of theimage includes analyzing a distribution of feature points of the image;and the parameter of the image capturing device is varied or maintainedto yield a wider distribution of feature points.
 18. The apparatus ofclaim 16, wherein: the application includes navigation of a movableobject that carries the image capturing device; the analysis of thetexture of the image includes analyzing a quality of feature points ofthe image; and the parameter of the image capturing device is varied ormaintained to yield a higher quality of feature points.
 19. Theapparatus of claim 11, wherein the input indicative of the desiredtexture quality is provided manually by a user or generated by theprocessor.
 20. An image processing method comprising: obtaining, by aprocessor, an input indicative of a desired image texture quality;receiving, by the processor, an image captured by an image capturingdevice; analyzing, by the processor, texture of the image; and varyingor maintaining, by the processor, a parameter of the image capturingdevice based on the analysis of the texture to yield the desired imagetexture quality.